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Table S4

AACR GENIE Data Guide

Supplemental Methods. Supplemental Table 1: ââ,¬â€¹Genomic Data Characterization by Center. Supplemental Table 2: ââ,¬â€¹Gene Panels Submitted by Each Center. Figure S1: Number of putative germline SNPs per sample, before and after uniform germline filtering. Figure S2ââ,¬â€¹. Distribution of total somatic mutation burden per sample stratified by sequencing panel. Figure S3: ââ,¬â€¹Log-scale comparison of mutation frequencies at hotspot sites between GENIE (data aggregated from all sequencing panels) and cancerhotspots.org (CHS) using a binomial test. Figure S4:ââ,¬â€¹ Comparison of mutation frequencies at hotspot sites in each GENIE sequencing panel with cancerhotspots.org (CHS) using a binomial test.

Background In the current study, we examined the real-world prevalence of highly pigmented advanced melanomas (HPMel) and the clinicopathologic, genomic, and ICPI biomarker signatures of this class of tumors. Materials and Methods Our case archive of clinical melanoma samples for which the ordering physician requested testing for both PD-L1 immunohistochemistry (IHC) and comprehensive genomic profiling (CGP) was screened for HPMel cases, as well as for non-pigmented or lightly pigmented advanced melanoma cases (LPMel). Results Of the 1268 consecutive melanoma biopsies in our archive that had been submitted for PD-L1 IHC, 13.0% (165/1268) were HPMel and 87.0% (1103/1268) were LPMel. In the HPMel cohort, we saw a significantly lower tumor mutational burden (TMB, median 8.8 mutations/Mb) than in the LPMel group (11.4 mut/Mb), although there was substantial overlap. In examining characteristic secondary genomic alterations (GA), we found that the frequencies of GA in TERTp, CDKN2A, TP53, and PTEN were significantly lower in the HPMel cases than in LPMel. A higher rate of GA in CTNNB1, APC, PRKAR1A, and KIT was identified in the HPMel cohort compared with LPMel. Conclusions In this study, we quantified the failure rates of melanoma samples for PD-L1 testing due to high melanin pigmentation and showed that CGP can be used in these patients to identify biomarkers that can guide treatment decisions for HPMel patients. Using this practical clinical definition for tumor pigmentation, our results indicate that HPMel are frequent at 13% of melanoma samples, and in general appear molecularly less developed, with a lower TMB and less frequent secondary GA of melanoma progression.

PURPOSE In real-world settings, patients with metastatic urothelial carcinoma (mUC) are often more frail than clinical trials, underscoring an unmet need to identify patients who might be spared first-line chemotherapy. We sought to determine whether tumor mutational burden (TMB) identifies patients with comparable or superior clinical benefit of first-line single-agent immune checkpoint inhibitors (ICPI) in real-world patients deemed cisplatin-unfit. METHODS Patients with mUC treated in first-line advanced setting (N = 401) received ICPI (n = 245) or carboplatin regiment without ICPI (n = 156) at physician's discretion in standard-of-care settings across approximately 280 US academic or community-based cancer clinics between March 2014 and July 2021. Deidentified data were captured into a real-world clinicogenomic database. All patients underwent testing using Foundation Medicine assays. Progression-free survival (PFS), time to next treatment (TTNT), and overall survival (OS) comparing ICPI versus chemotherapy were adjusted for known treatment assignment imbalances using propensity scores. RESULTS TMB ≥ 10 was detected in 122 of 401 (30.4%) patients. Among patients receiving ICPI, those with TMB ≥ 10 had more favorable PFS (HR, 0.59; 95% CI, 0.41 to 0.85), TTNT (HR, 0.59; 95% CI, 0.43 to 0.83), and OS (HR, 0.47; 95% CI, 0.32 to 0.68). Comparing ICPI versus carboplatin, adjusting for imbalances, patients with TMB ≥ 10 had more favorable PFS (HR, 0.51; 95% CI, 0.32 to 0.82), TTNT (HR, 0.56; 95% CI, 0.35 to 0.91), and OS (HR, 0.56; 95% CI, 0.29 to 1.08) on ICPI versus chemotherapy, but not TMB < 10. Comparisons unadjusted for imbalances had similar associations. CONCLUSIONS In real-world settings, mUC patients with TMB ≥ 10 have more favorable outcomes on first-line single-agent ICPI than carboplatin, adding clinical validity to TMB assessed by an existing US Food and Drug Administration–approved platform.

The most useful biomarkers for clinical decision-making identify patients likely to have improved outcomes with one treatment vs another.To evaluate treatment class-specific outcomes of patients receiving immune checkpoint inhibitor (ICI) vs taxane chemotherapy by tumor mutational burden (TMB).This comparative effectiveness analysis of clinical variables and outcomes used prospectively defined biomarker-stratified genomic data from a deidentified clinicogenomic database. Data included men with previously treated metastatic castration-resistant prostate cancer (mCRPC) receiving ICI or single-agent taxane chemotherapy from January 2011 to April 2021 at approximately 280 US academic or community-based cancer clinics (approximately 800 sites of care). Data were analyzed from July to August 2021.Single-agent ICI or single-agent taxanes. Treatments were assigned at discretion of physician and patient without randomization. Imbalances of known factors between treatment groups were adjusted with propensity weighting.Prostate-specific antigen (PSA) response, time to next therapy (TTNT), and overall survival (OS).A total of 741 men (median [IQR], 70 [64-76] years) with mCRPC received comprehensive genomic profiling and were treated with ICI or single-agent taxane therapy. At baseline, the median (IQR) PSA level was 79.4 (19.0-254) ng/mL, 108 men (18.8%) had Eastern Cooperative Oncology Group Performance Status scores of 2 or greater, and 644 men (86.9%) had received prior systemic treatments for mCRPC. A total of 45 patients (6.1%) received ICI therapy and 696 patients (93.9%) received taxane therapy. Among patients with TMB of fewer than 10 mutations per megabase (mt/Mb) receiving ICI, compared with those receiving taxanes, had worse TTNT (median [IQR], 2.4 [1.1-3.2] months vs 4.1 [2.2-6.3] months; hazard ratio [HR], 2.65; 95% CI, 1.78-3.95; P .001). In contrast, for patients with TMB of 10 mt/Mb or greater, use of ICIs, compared with use taxanes, was associated with more favorable TTNT (median [IQR], 8.0 [3.4 to unknown] months vs 2.4 [2.4-7.3] months; HR, 0.37, 95% CI, 0.15-0.87; P = .02) and OS (median 19.9 [8.06 to unknown] months vs 4.2 [2.69 - 6.12] months; HR, 0.23; 95% CI, 0.10-0.57; P = .001). Among all 741 patients, 44 (5.9%) had TMB of 10 mt/Mb or greater, 22 (3.0%) had high microsatellite instability, and 20 (2.7%) had both. Treatment interactions with TMB of 10 mt/Mb or greater (TTNT: HR, 0.10; 95% CI, 0.32-0.31; P .001; OS: HR, 0.25; 95% CI, 0.076-0.81; P = .02) were stronger than high microsatellite instability alone (TTNT: HR, 0.12; 95% CI, 0.03-0.51; P = .004; OS: HR, 0.38; 95% CI, 0.13-1.12; P = .08).In this comparative effectiveness study, ICIs were more effective than taxanes in patients with mCRPC when TMB was 10 mt/Mb or greater but not when TMB was fewer than 10 mt/Mb. The results add validity to the existing TMB cutoff of 10 mt/Mb for ICI use in later lines of therapy, and suggest that ICIs may be a viable alternative to taxane chemotherapy for patients with mCRPC with high TMB.

PURPOSEOur goal was to identify the opportunities and challenges in analyzing data from the American Association of Cancer Research Project Genomics Evidence Neoplasia Information Exchange (GENIE), a multi-institutional database derived from clinically driven genomic testing, at both the inter- and the intra-institutional level. Inter-institutionally, we identified genotypic differences between primary and metastatic tumors across the 3 most represented cancers in GENIE. Intra-institutionally, we analyzed the clinical characteristics of the Vanderbilt-Ingram Cancer Center (VICC) subset of GENIE to inform the interpretation of GENIE as a whole.METHODSWe performed overall cohort matching on the basis of age, ethnicity, and sex of 13,208 patients stratified by cancer type (breast, colon, or lung) and sample site (primary or metastatic). We then determined whether detected variants, at the gene level, were associated with primary or metastatic tumors. We extracted clinical data for the VICC subset from VICC’s clinical data warehouse. Treatment exposures were mapped to a 13-class schema derived from the HemOnc ontology.RESULTSAcross 756 genes, there were significant differences in all cancer types. In breast cancer, ESR1 variants were over-represented in metastatic samples (odds ratio, 5.91; q < 10−6). TP53 mutations were over-represented in metastatic samples across all cancers. VICC had a significantly different cancer type distribution than that of GENIE but patients were well matched with respect to age, sex, and sample type. Treatment data from VICC was used for a bipartite network analysis, demonstrating clusters with a mix of histologies and others being more histology specific.CONCLUSIONThis article demonstrates the feasibility of deriving meaningful insights from GENIE at the inter- and intra-institutional level and illuminates the opportunities and challenges of the data GENIE contains. The results should help guide future development of GENIE, with the goal of fully realizing its potential for accelerating precision medicine.

AKT inhibitors have promising activity in AKT1E17K-mutant estrogen receptor (ER)–positive metastatic breast cancer, but the natural history of this rare genomic subtype remains unknown. Utilizing AACR Project GENIE, an international clinicogenomic data-sharing consortium, we conducted a comparative analysis of clinical outcomes of patients with matched AKT1E17K-mutant (n = 153) and AKT1–wild-type (n = 302) metastatic breast cancer. AKT1-mutant cases had similar adjusted overall survival (OS) compared with AKT1–wild-type controls (median OS, 24.1 vs. 29.9, respectively; P = 0.98). AKT1-mutant cases enjoyed longer durations on mTOR inhibitor therapy, an observation previously unrecognized in pivotal clinical trials due to the rarity of this alteration. Other baseline clinicopathologic features, as well as durations on other classes of therapy, were broadly similar. In summary, we demonstrate the feasibility of using a novel and publicly accessible clincogenomic registry to define outcomes in a rare genomically defined cancer subtype, an approach with broad applicability to precision oncology. Significance: We delineate the natural history of a rare genomically distinct cancer, AKT1E17K-mutant ER-positive breast cancer, using a publicly accessible registry of real-world patient data, thereby illustrating the potential to inform drug registration through synthetic control data. See related commentary by Castellanos and Baxi, p. 490.

Background We sought to characterize response to immune checkpoint inhibitor (ICI) in non-squamous non-small cell lung cancer (NSCLC) across various CD274 copy number gain and loss thresholds and identify an optimal cutoff. Materials and Methods A de-identified nationwide (US) real-world clinico-genomic database was leveraged to study 621 non-squamous NSCLC patients treated with ICI. All patients received second-line ICI monotherapy and underwent comprehensive genomic profiling as part of routine clinical care. Overall survival (OS) from start of ICI, for CD274 copy number gain and loss cohorts across varying copy number thresholds, were assessed. Results Among the 621 patients, patients with a CD274 CN greater than or equal to specimen ploidy +2 (N = 29) had a significantly higher median (m) OS when compared with the rest of the cohort (N = 592; 16.1 [8.9-37.3] vs 8.6 [7.1-10.9] months, hazard ratio (HR) = 0.6 [0.4-1.0], P-value = .05). Patients with a CD274 copy number less than specimen ploidy (N = 299) trended toward a lower mOS when compared to the rest of the cohort (N = 322; 7.5 [5.9-11.3] vs 9.6 [7.9-12.8] months, HR = 0.9 [0.7-1.1], P-value = .3). Conclusion This work shows that CD274 copy number gains at varying thresholds predict different response to ICI blockade in non-squamous NSCLC. Considering these data, prospective clinical trials should further validate these findings, specifically in the context of PD-L1 IHC test results.

PURPOSE Patients with breast cancer spend a large amount of time and effort receiving treatment. When the number of health care tasks exceeds a patient’s ability to manage that workload, they could become overburdened, leading to decreased plan adherence. We used electronic health record data to retrospectively assess dimensions of treatment workload related to outpatient encounters, commuting, and admissions. METHODS Using tumor registry and scheduling data, we evaluated the sensitivity of treatment workload measures to detect expected differences in breast cancer treatment burden by stage. We evaluated the impact of the on-body pegfilgrastim injector on the treatment workload of patients undergoing a specific chemotherapy protocol. RESULTS As hypothesized, patients with higher stage cancer experienced higher treatment workload. Over the first 18 months after diagnosis, patients with stage III disease spent a median of 81 hours (interquartile range [IQR], 39 to 113 hours) in outpatient clinics, commuted 61 hours (IQR, 32 to 86 hours), and spent $1,432 (IQR, $690 to $2,552) in commuting costs. In contrast, patients with stage I disease spent a median of 29 hours (IQR, 18 to 46 hours in clinic), commuted for 34 hours (IQR, 19 to 55 hours), and spent $834 (IQR, $389 to $1,649) in commuting costs. In addition, we substantiated claims that the pegfilgrastim on-body injector was effective in reducing some dimensions of workload such as unique appointment days. CONCLUSION Treatment workload measures capture an important dimension in the experience of patients with cancer. Patients and health care organizations can use workload measures to plan and allocate resources, leading to higher quality and better coordinated care.

331 Background: Personalized breast cancer risk assessment (CRA) and genetic counseling/testing for women have been shown to improve interventional and clinical outcomes. However, despite CRA being routinely offered to all women at our institution, not all women choose to opt into the program. This study evaluated the demographic distribution of patients that opted out of the CRA to identify potential components influencing this substantial decision. Methods: Our medical center, located in four urban and suburban locations, piloted a clinical framework to provide CRA to all women between the ages of 25 and 75 with a qualifying mammogram. In this single institution retrospective study, we analyzed differences among patients who opted out of the program between July 20, 2020and July 19, 2021. Data elements extracted from the electronic medical record include race, primary language, location of care, and decision to partake in CRA. Overall Chi-square tests and all pairwise comparisons with Bonferroni correction were used to statistically determine the impact of various demographics on opting out. Results: 18726 women met criteria for inclusion and 2717 (14.5%) declined CRA. Within their respective racial groups, 122 (17.6%) Asian, 1175 (16.3%) Black, 455 (14.2%) Hispanic, 112 (15%) Other, and 853 (12.4%) White opted out. Within their identified primary language, 2370 (13.7%) English, 231 (23.1%) Spanish, 35 (19.7%) Bilingual, and 81 (35.2%) Other opted out. Based on their setting of care, 837 (16.8%) Location A, 1436 (14.2%) Location B, 270 (13.8%) Location C, and 174 (10.6%) Location D opted out. The differences between demographics and opting out were statistically significant for all analyses (p

Objective Asynchronous messaging is an integral aspect of communication in clinical settings, but imposes additional work and potentially leads to inefficiency. The goal of this study was to describe the time spent using the electronic health record (EHR) to manage asynchronous communication to support breast cancer care coordination. Methods We analyzed 3 years of audit logs and secure messaging logs from the EHR for care team members involved in breast cancer care at Vanderbilt University Medical Center. To evaluate trends in EHR use, we combined log data into sequences of events that occurred within 15 minutes of any other event by the same employee about the same patient. Results Our cohort of 9,761 patients were the subject of 430,857 message threads by 7,194 employees over a 3-year period. Breast cancer care team members performed messaging actions in 37.5% of all EHR sessions, averaging 29.8 (standard deviation [SD] = 23.5) messaging sessions per day. Messaging sessions lasted an average of 1.1 (95% confidence interval: 0.99–1.24) minutes longer than nonmessaging sessions. On days when the cancer providers did not otherwise have clinical responsibilities, they still performed messaging actions in an average of 15 (SD = 11.9) sessions per day. Conclusion At our institution, clinical messaging occurred in 35% of all EHR sessions. Clinical messaging, sometimes viewed as a supporting task of clinical work, is important to delivering and coordinating care across roles. Measuring the electronic work of asynchronous communication among care team members affords the opportunity to systematically identify opportunities to improve employee workload.

Patients with breast cancer spend significant time1, effort, and financial resources2 to combat the disease for years after their diagnosis. The large volume of healthcare tasks can cause patients to become overburdened, leading to reduced adherence with care plans and worse outcomes3. On the other hand, certain patient characteristics such as physical resilience, financial well-being, and supportive family environments increase patients' capacity to manage care4. Assessing treatment burden and capacity when prescribing care has been applied to populations such as diabetes patients5. We are investigating this paradigm in treatment of patients with breast cancer. The goal of this preliminary study is to identify significant factors that contribute to treatment burden, capacity to manage care, and outcomes of overburden for patients with breast cancer. Examples of treatment burden, capacity to manage care, and outcomes of overburden in patients with breast cancerTreatment burdenCapacity to manage careOutcomes of overburdenTraveling long distances for careAccess to reliable transportationReduced spending on food, utilities, or other necessitiesPaying for child care during chemotherapyFlexibility in informal caregivers' schedulesMissed appointment with medical oncologistRemembering to take medications with mealsMedical understanding or knowledgeWorse than expected side effectsReporting adverse eventsProficiency with mobile deviceTrip to emergency room Through literature review, interviews with survivors, and expert panels of navigators and providers, we will develop a survey instrument given to patients at the time of diagnosis. The survey will assess patient capacity and help providers give treatment options based on attributes of the patient. Additionally, we will attempt to correlate survey results with treatment burden measures derived from electronic health record data at a population level1. With treatment personalized for patient capacity, patients should be better able to adhere to care plans leading to improved quality of life during treatment and beyond. Acknowledgements: The authors would like to thank Cheryl Jernigan, our patient advocate mentor, for her guidance in this project. We would also like to thank the Susan G. Komen Foundation for their support of this research. References: 1. Cheng, A. C. & Levy, M. A. Data Driven Approach to Burden of Treatment Measurement: A Study of Patients with Breast Cancer. AMIA Annu. Symp. proceedings. AMIA Symp. 2016, 1756–1763 (2016). 2. Zafar, S. Y. et al. The financial toxicity of cancer treatment: a pilot study assessing out-of-pocket expenses and the insured cancer patient's experience. Oncologist. 18, 381–90 (2013). 3. Mair, F. S. & May, C. R. Thinking about the burden of treatment. BMJ. 349, g6680–g6680 (2014). 4. Boehmer, K. R., Shippee, N. D., Beebe, T. J. & Montori, V. M. Pursuing Minimally Disruptive Medicine: Correlation of patient capacity with disruption from illness and healthcare-related demands. J. Clin. Epidemiol. (2016). 5. Ishii, H. et al. Reproducibility and Validity of a Questionnaire Measuring Treatment Burden on Patients with Type 2 Diabetes: Diabetic Treatment Burden Questionnaire (DTBQ). Diabetes Ther. 9, 1001–1019 (2018). Citation Format: Cheng A, Levy M. Treatment burden and capacity to manage care among patients with breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr OT2-10-01.

5063 Background: NEPC is a high-grade aggressive form of prostate cancer. We queried whether RB1 mutation status would impact the genomic features of NEPC in RB1 mutated vs non-mutated cases. Methods: From a series of 13,496 cases of clinically advanced PC, we identified 415 cases (3.1%) with a diagnosis of small cell PC or NEPC as determined by the submitting physician. They were sequenced using a hybrid capture-based FDA-approved clinical genomic profiling (CGP) assay to detect all classes of genomic alterations (GA). Tumor mutational burden (TMB) was determined on 0.8 Mbp of sequenced DNA and microsatellite instability (MSI) was determined on 95 loci. PD-L1 expression was determined by IHC (Dako 22C3) with low tumor cell positive staining 1-49% and high staining ≥50% expression. Results: 253 (61%) of NEPC feature GA in RB1 (RB1 mut+). This contrasts with a 5.8% frequency of RB1 GA in the non-NEPC (P

3535 Background: Approximately 20% of colorectal cancer (CRC) has an activating mutation in the PIK3CA oncogene. PIK3CA codes for the catalytic subunit of phosphoinositide 3-kinase alpha (PI3Kα), which ultimately activates the AKT and mammalian target of rapamycin (mTOR) pathway. The PI3Kα inhibitor alpelisib has been approved for breast cancer where a single PIK3CA activating mutation is sufficient for response. However, two activating mutations (multi-hit) in the PIK3CA allele substantially increases PI3Kα signaling compared to single hot spot mutations and results in exceptional response to PI3Kα inhibition. We aimed to identify the prevalence of PIK3CA multi-hit mutations in CRC to identify patients potentially susceptible to PI3K inhibitors. Methods: Tissue-based comprehensive genomic profiling (CGP) was performed in a Clinical Laboratory Improvement Amendments (CLIA)-certified, CAP (College of American Pathologists)-accredited laboratory (Foundation Medicine Inc., Cambridge, MA, USA) on all-comers during the course of routine clinical care from 2013-2021. Approval was obtained from the Western Institutional Review Board (Protocol No. 20152817). Hybrid capture was carried out for at least 324 cancer-related genes, including PIK3CA. Results: We identified 48,836 patients with advanced CRC who underwent Foundation Medicine testing and 846 (1.7%) patients with multi-hit PIK3CA mutations. Additional clinical and molecular data was available for 41,154 of these patients of which 710 (1.7%) had multi-hit PIK3CA and 7627 (19%) had any deleterious PIK3CA mutation . The local colon tumor was used for sequencing in 70% of cases and a separate site in 30% of cases. Patients with PIK3CA multi-hit mutations were 53% male with a median age of 60 (interquartile range 50-70). The microsatellite status was available for 697 of 710 patients with multi-hit PIK3CA and 123/697 (18%) were microsatellite instability-high. The Table outlines the genes with cooccurring mutations of >10% prevalence for multi-hit PIK3CA CRC, including the clinically relevant mutations in KRAS (65%) and BRAF (13%). The four most common PIK3CA variants were H1047R (9.8%), E545K (9.2%), E542K (9.0%) and R88Q (7.1%). The most common variant pair was E542K with E545K in 4.7% of multi-hit cases. Conclusions: Double-hit mutations in PIK3CA are seen in 1.7% of advanced CRC patients and may represent a subset of patients that may have enhanced sensitivity to PI3K inhibitors. Given the high prevalence of CRC in the United States and worldwide this represents a clinically meaningful prevalence of multi-hit PIK3CA. Future investigation on the clinical utility of PI3K inhibitors may be warranted in multi-hit PIK3CA CRC.[Table: see text]

e18772 Background: Dense breast tissue is known to obscure malignancy on mammography and is considered an independent risk factor for breast cancer. Because of its clinical significance, breast density has been incorporated into cancer risk assessment tools, supplemental screening recommendations, and patient notification laws. Changes in density—whether due to intrinsic changes in breast tissue over time or to inter-radiologist variability in interpreting mammographic results—can therefore have significant implications for patients. The purpose of this study was to quantify longitudinal patterns of breast density change. Methods: This retrospective cohort study tracked breast density changes among 37,156 patients who received multiple mammograms at a large academic medical center between October 1, 2015 and June 29, 2021. Breast Imaging-Reporting and Data System (BI-RADS) density categories A (least dense) through D (most dense), visually determined by radiologists at the time of screening, were abstracted from electronic medical records and dichotomized into one of two density statuses: non-dense (categories A and B) and dense (categories C and D). A sequence analysis of longitudinal changes between the two density statuses was performed using SQL. Results: The majority (91.3%, n = 33,920) of patients maintained the same density status (dense or non-dense) throughout the six-year study period, while 8.7% of participants (n = 3,236) experienced between one and six density status changes. Among patients who experienced any density status change, the vast majority (96.7%, n = 3,131) moved exclusively between BI-RADS categories B and C, the intermediate density categories (Table). Frequency of density status change was positively correlated with average number of mammograms performed per patient. Conclusions: Although most patients maintain a consistent breast density status, some will experience multiple status changes even over this relatively short timeframe (< 6 years), with more frequent changes being associated with more frequent mammograms. The overwhelming majority of patients whose density status changed moved exclusively between BI-RADS categories B and C, suggesting a role for inter-radiologist variability in visual assessments of intermediate-density tissue. These findings have implications for how density status changes are incorporated into patient education, risk assessment, and supplemental screening recommendations.[Table: see text]

1025 Background: ER-PR+Her2- breast cancer is a rare subtype occurring at approximately 1% of all breast carcinomas. Most of these cancers behave in an aggressive fashion with limited benefit from anti estrogen therapy, similar to triple negative breast cancer (TNBC). Better characterization of these tumors is needed for predicting clinical behavior, response to endocrine therapy, and eligibility for clinical trials. Here we sought to evaluate the mutational profile of a well curated set of ER-PR+HER2- metastatic breast cancers and compare to other receptor phenotypes. Methods: 2049 consecutive breast cancers submitted to Foundation Medicine for comprehensive genomic profiling (CGP) were included. ER, PR and HER2 expression were abstracted from submitted pathology reports. Cases without complete ER, PR and HER2 information in pathology reports were excluded. CGP was performed as previously described (Frampton, 2013). Results: Patient ages were similar across subgroups. Generally, ER-PR+HER2- tumors were rare (n = 23, 1.1%) and most similar to TNBC in their genomic profiles. These tumors harbored high rates of TP53 and BRCA1 alterations and low rates of PIK3CA, ESR1, and CDH1 alterations. Genomic loss of heterozygosity (gLOH) was similar in the ER-PR+HER2- and ER+PR+HER2-subtypes (8.18% and 8.66% respectively), and lower than TNBC (17.19%). Notably, a high rate of RB1 alterations were identified in the ER-PR+HER2- patients (13%, 3/23), numerically higher than the other subtypes. EGFR, MET, PTEN, CDKN2A and KRAS alterations were also observed at a higher frequency in ER-PR+Her2- cancers (8.7, 4.2, 39.1, 13.0 and 13.0% respectively) relative to the other subtypes. IO drug biomarkers including MSI, TMB and PD-L1 IHC were similar among the groups. Conclusions: The mutational profile for ER-PR+Her2- metastatic breast cancer more closely resembles TNBC than ER+ breast cancer. These data suggest molecular profiling may be a useful adjunct to optimize treatment strategies for this rare subset of cancers. Based on molecular characteristics, we recommend including ER-PR+Her2- patients in clinical trials for TNBC. Finally, genes including RB1, CKDN2A, PTEN, EGFR and MET are mutated at higher frequency in ER-PR+Her2- cancers than other subsets, suggesting unique biology with potential therapeutic implications. [Table: see text]

e18567 Background: Personalized breast cancer risk assessment (CRA) and genetic counseling/testing for women have been shown to improve interventional and clinical outcomes. However, despite CRA being routinely offered to all women at our institution, not all women choose to opt into the program. This study evaluated the demographic distribution of patients that opted out of the CRA to identify potential components influencing this substantial decision. Methods: Our medical center, located in four urban and suburban locations, piloted a clinical framework to provide CRA to all women between the ages of 25 and 75 with a qualifying mammogram. In this single institution retrospective study, we analyzed differences among patients who opted out of the program between July 20, 2020 and July 19, 2021. Data elements extracted from the electronic medical record include race, primary language, location of care, and decision to partake in CRA. Overall Chi-square tests and all pairwise comparisons with Bonferroni correction were used to statistically determine the impact of various demographics on opting out. Results: 18726 women met criteria for inclusion and 2717 (14.5%) declined CRA. Within their respective racial groups, 122 (17.6%) Asian, 1175 (16.3%) Black, 455 (14.2%) Hispanic, 112 (15%) Other, and 853 (12.4%) White opted out. Within their identified primary language, 2370 (13.7%) English, 231 (23.1%) Spanish, 35 (19.7%) Bilingual, and 81 (35.2%) Other opted out. Based on their setting of care, 837 (16.8%) Location A, 1436 (14.2%) Location B, 270 (13.8%) Location C, and 174 (10.6%) Location D opted out. The differences between demographics and opting out were statistically significant for all analyses (p < .0001). Results shown in Table below. Conclusions: The main variables mediating the decision to opt out of CRA are a non-English primary language, non-White race, and location of care, potentially due to inadequate interpretation services, barriers to education, and staffing shortages. Thus, these women are less likely to receive genetic counseling/testing or supplemental screening. Our study is the first to investigate the factors contributing to the uptake of risk assessment. Further study is needed to determine the clinical impact of this striking disparity.[Table: see text]

1027 Background: Advanced breast cancer in women < 40 years is more aggressive, with worse prognosis and disease-free survival, compared to older women with the disease. With increasing availability of targeted and immune therapies, we aimed to compare genomic alterations (GA) using comprehensive genomic profiling (CGP) of tumor tissue. Methods: We analyzed 2,049 breast cancers submitted to Foundation Medicine for CGP. Hybrid-capture based CGP was performed to evaluate all classes of GA. Tumor mutational burden (TMB) was determined on at least 0.8 Mbp of sequenced DNA and microsatellite instability was determined on at least 95 loci. Tumor cell PD-L1 expression (defined as tumor proportion score >/= 1) was determined by IHC (Dako 22C3). We identified 28 (1.37%) patients /= 40 years. Breast tissue was used for CGP in 69.5% of cases and remainder of specimens were lymph node, metastatic, or unspecified. Results: Breast tumors were less likely to be estrogen receptor positive in younger women (54% of those /= 40 years) and more likely to be triple negative (43%, 33%, 26.1% in the same respective groups). There was no clear pattern in HER2+ status by age (0%, 15.1%, 7.2%). Younger women had higher rates of BRCA1 (17.9%, 10.1%, 2.6%), BRCA2 mutations (7.10%, 5.70%, 4.1%), and RB1 mutations (14.3%, 9.4%, 6.1%), and lower rates of CDH1 (7.1%, 5%, 15.4%) and PIK3CA mutations (17.9%, 17.6%, 40.0%). Younger women were more likely to have PD-L1 expression (55.6%, 54.4%, 51.5%) but had lower frequencies of TMB >10 (0.0%, 5.0%, 8.7%). Differences are statistically significant in BRCA1, CDH1, and PIK3CA. Conclusions: These findings confirm that young women with breast cancer have actionable GA. Different mutational profiles may support differential use of targeted and immune therapies. Statistically and clinically significant differences include higher BRCA1 mutations which may lend to PARP inhibitor use and lower PIK3CA mutations which may reduce alpelisib use. Higher RB1 mutations and immunotherapy biomarker differences were not statistically significant. However, these may clinically translate into CDK4/6 resistance and reduced immunotherapy options, respectively. [Table: see text]

The American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) is an international data-sharing consortium focused on enabling advances in precision oncology through the gathering and sharing of tumor genetic sequencing data linked with clinical data. The project’s history, operational structure, lessons learned, and institutional perspectives on participation in the data-sharing consortium are reviewed. Individuals involved with the inception and execution of AACR Project GENIE from each member institution described their experiences and lessons learned. The consortium was conceived in January 2014 and publicly released its first data set in January 2017, which consisted of 18,804 samples from 18,324 patients contributed by the eight founding institutions. Commitment and contributions from many individuals at AACR and the member institutions were crucial to the consortium’s success. These individuals filled leadership, project management, informatics, data curation, contracts, ethics, and security roles. Many lessons were learned during the first 3 years of the consortium, including on how to gather, harmonize, and share data; how to make decisions and foster collaboration; and how to set the stage for continued participation and expansion of the consortium. We hope that the lessons shared here will assist new GENIE members as well as others who embark on the journey of forming a genomic data–sharing consortium.

Purpose of Review The emergency medicine and critical care needs of the COVID-19 pandemic forced a sudden and dramatic disruption of cancer screening and treatment programs in the USA during the winter and spring of 2020. This review commentary addresses the impact of the pandemic on racial/ethnic minorities such as African Americans and Hispanic-Latina Americans, with a focus on factors related to breast cancer. Recent Findings African Americans and Hispanic-Latina Americans experienced disproportionately higher morbidity and mortality from COVID-19; many of the same socioeconomic and tumor biology/genetic factors that explain breast cancer disparities are likely to account for COVID-19 outcome disparities. Summary The breast cancer clinical and research community should partner with public health experts to ensure participation of diverse patients in COVID-19 treatment trials and vaccine programs and to overcome COVID-19-related breast health management delays that are likely to have been magnified among African Americans and Hispanic-Latina Americans.

Importance There is an enormous and growing amount of data available from individual cancer cases, which makes the work of clinical oncologists more demanding. This data challenge has attracted engineers to create software that aims to improve cancer diagnosis or treatment. However, the move to use computers in the oncology clinic for diagnosis or treatment has led to instances of premature or inappropriate use of computational predictive systems. Objective To evaluate best practices for developing and assessing the clinical utility of predictive computational methods in oncology. Evidence Review The National Cancer Policy Forum and the Board on Mathematical Sciences and Analytics at the National Academies of Sciences, Engineering, and Medicine hosted a workshop to examine the use of multidimensional data derived from patients with cancer and the computational methods used to analyze these data. The workshop convened diverse stakeholders and experts, including computer scientists, oncology clinicians, statisticians, patient advocates, industry leaders, ethicists, leaders of health systems (academic and community based), private and public health insurance carriers, federal agencies, and regulatory authorities. Key characteristics for successful computational oncology were considered in 3 thematic areas: (1) data quality, completeness, sharing, and privacy; (2) computational methods for analysis, interpretation, and use of oncology data; and (3) clinical infrastructure and expertise for best use of computational precision oncology. Findings Quality control was found to be essential across all stages, from data collection to data processing, management, and use. Collecting a standardized parsimonious data set at every cancer diagnosis and restaging could enhance reliability and completeness of clinical data for precision oncology. Data completeness refers to key data elements such as information about cancer diagnosis, treatment, and outcomes, while data quality depends on whether appropriate variables have been measured in valid and reliable ways. Collecting data from diverse populations can reduce the risk of creating invalid and biased algorithms. Computational systems that aid clinicians should be classified as software as a medical device and thus regulated according to the potential risk posed. To facilitate appropriate use of computational methods that interpret high-dimensional data in oncology, treating physicians need access to multidisciplinary teams with broad expertise and deep training among a subset of clinical oncology fellows in clinical informatics. Conclusions and Relevance Workshop discussions suggested best practices in demonstrating the clinical utility of predictive computational methods for diagnosing or treating cancer.

547 Background: There is an unmet need to identify metastatic urothelial carcinoma (mUC) patients who might be spared chemotherapy in 1st line. Anti-PD-(L)1 immune checkpoint inhibitors (ICPI) alone without chemotherapy did not show superiority to platinum-based chemotherapy in ITT populations of DANUBE, KEYNOTE-361, and IMvigor130. However, DANUBE and IMvigor130 reported secondary subgroup analyses, both suggesting enhanced benefit for ICPI vs. chemotherapy in patients with tumor mutational burden (TMB) ≥ 10 (mutations/megabase), using same cutoff and assay as pan-tumor CDx for pembrolizumab approved in later lines of therapy. We sought to determine if TMB ≥ 10 identified a group of enhanced relative ICPI benefit (single-agent anti-PD[L]1 w/o chemo) in real-world settings where patients are less eligible for chemotherapy. Methods: Association of genomic data with clinical variables and outcomes in cohort of patients with mUC treated January 2011- April 2021. Longitudinal de-identified clinical data from approximately 280 U.S. academic or community-based cancer clinics were derived from electronic health records, curated via technology-enabled abstraction by Flatiron Health and linked to genomic testing by Foundation Medicine. 849 1st line mUC patients received either ICPI (n = 307) or chemotherapy (n = 542) at physician’s discretion in standard of care settings. All patients underwent genomic testing using Foundation Medicine comprehensive genomic profiling assays (FoundationOne© or FoundationOne©CDx). PFS and OS were assessed unadjusted and adjusted for imbalances using propensity scores. Results: 273 of 849 (32.2%) patients had TMB ≥ 10. Pre-therapy characteristics: patients assigned ICPI vs. chemotherapy had comparable TMB, primary disease site, histology, smoking status, and PD-L1 staining, but were generally older (median years: 72 vs. 67, p < 0.001), higher ECOG scores (p < 0.001), lower CrCl (median ml/min: 49.8 vs. 59.7, p < 0.001), and lower hemoglobin (median: 11.5 vs. 12.1, p < 0.001). Unadjusted, TMB ≥ 10 group showed more favorable PFS (HR: 0.72, 95%CI: 0.52 – 0.99, p = 0.041) and OS (HR: 0.70, 95%CI: 0.49 – 0.1, p = 0.048) for ICPI vs. chemotherapy despite imbalances favoring outcomes on chemotherapy. ICPI vs. chemotherapy outcomes adjusted for imbalances: TMB ≥ 10 group showed more favorable PFS (HR: 0.65, 95%CI: 0.45 – 0.95, p = 0.026) and OS (HR: 0.61, 95%CI: 0.39 – 0.93, p = 0.022), while TMB < 10 had comparable or worse PFS (HR: 1.30, 95%CI: 0.98 – 1.72, p = 0.06) and OS (HR: 1.03; 95%CI: 0.78– 1.34, p = 0.85). Conclusions: In real-world settings, 1st line mUC patients with TMB ≥ 10 have more favorable PFS and OS on single agent ICPI than chemotherapy, adding clinical validity to TMB as a predictive biomarker in patient populations less eligible for chemotherapy than reported trials.

162 Background: The most useful biomarkers for clinical decision-making identify patients likely to have improved outcomes on one treatment vs. another. To date, no study has compared the treatment class-specific outcomes of patients with metastatic castration-resistant prostate cancer (mCRPC) on Immune Checkpoint Inhibitor (ICPI) vs. taxane chemotherapy by tumor mutational burden (TMB, mutations/megabase). Methods: Association of genomic data with clinical variables and outcomes in cohort of patients with mCRPC treated January 2011- April 2021. Longitudinal de-identified clinical data from ̃280 U.S. academic or community-based cancer clinics were derived from electronic health records, curated via technology-enabled abstraction by Flatiron Health and linked to genomic testing by Foundation Medicine (FoundationOne or FoundationOne CDx assays). 45 patients (14 with TMB ≥ 10, 31 TMB < 10) received single-agent anti-PD1 axis ICPI, 696 (30 with TMB ≥ 10, 666 TMB < 10) received single-agent taxanes, at discretion of physician without randomization. For time to next therapy (TTNT) and overall survival (OS) assessments, imbalances between treatment groups were adjusted with propensity weighting. Results: Overall cohort: Median age: 70 (IQR: 64 – 76), median PSA: 79.4 (IQR: 19.0 – 254), 108 (18.8%) were ECOG 2+, 644 (86.9%) had received prior systemic treatments for mCRPC. Patients receiving ICPI vs. taxanes had comparable pre-therapy age, PSA, hemoglobin, alkaline phosphatase, prior second generation novel hormonal therapy use, and prior opioid use, but higher TMB (median 3.5, IQR: 1.7 – 15 vs. median 2.5, IQR 1.3 – 3.8, p < 0.001), higher ECOG scores (0, 1, 2+ respectively 13.9%, 55.6%, 30.6% vs. 29.4%, 52.6%, 18.8%, p = 0.057), and greater prior taxane use (73.3% vs. 53.7%, p = 0.01). Among patients with evaluable PSA response, no difference was observed on taxanes by TMB level. No patients had PSA decline ≥ 50% on ICPI if TMB < 10, 4 of 9 with TMB ≥ 10 had PSA decline ≥ 50%. TMB < 10 receiving ICPI vs. taxanes had worse TTNT (median 2.4 vs. 4.1 months; HR: 2.7, 95%CI: 1.7 – 4.0, p < 0.001) and numerically worse OS (median 4.2 vs. 6.0 months, HR: 1.08; 95%CI: 0.68– 1.7, p = 0.73). In contrast, for TMB ≥ 10 ICPI vs. taxane use was associated with more favorable TTNT (median 8.0 vs. 2.4 months; HR: 0.37, 95%CI: 0.15 – 0.87, p = 0.022) and OS (median 19.9 vs. 4.2 months; HR: 0.23, 95%CI: 0.10 – 0.57, p = 0.0085). Among all 741 patients, 44 had TMB ≥ 10, 22 had high microsatellite instability (MSI-H), 20 had both. Treatment interactions with TMB ≥ 10 (TTNT: p < 0.001, OS: p = 0.021) were stronger than MSI-H (TTNT: p = 0.0038, OS: p = 0.080). Conclusions: The results suggest ICPI may be a viable alternative to taxane chemotherapy for patients with mCRPC with TMB ≥ 10, adding validity to existing FDA approved platform and pan-tumor TMB score cutoff of 10.

604 Background: MTAP loss is represented across a wide variety of cancer types including PDAC and is an emerging target for synthetic lethality-based cancer therapies. Preclinically, MTAP loss leads to the accumulation of 2-methylthioadenosine, reduced protein arginine N-methyltransferase 5 (PRMT5) methylation activity and increased vulnerability to targeting of the methionine adenosyltransferase IIα (MAT2A)/ PRMT5 axis. In addition, 9p21 loss, homozygous co-deletion of MTAP/CDKN2A or homozygous deletion of either gene have been associated with an immunologically “cold” tumor microenvironment, primary resistance to anti PD(L)1 immunotherapy (IO) and poor prognosis phenotype (Han G, Nat Commun 2021). We investigated concurrent mutations and immune biomarkers in clinical PDAC samples with MTAP-loss versus -intact status. Methods: From a series of 177705 consecutive cases, we performed comprehensive genomic profiling on 9423 cases of PDAC using an FDA-approved assay (F1CDx) to evaluate all classes of genomic alterations (GA). Tumor mutational burden (TMB) was determined on up to 1.1 Mbp of sequenced DNA and microsatellite instability (MSI) was determined on 114 loci. PD-L1 expression was determined by immunohistochemistry (Dako 22C3). Furthermore, we correlated pertinent findings within a database of 16558 cases of clinically advanced cancer with MTAP loss. Results: 2003 (21.3%) of 9423 PDAC demonstrated MTAP-loss. Similar gender, age and number of GA per tumor were observed between MTAP-loss and -intact groups. Frequencies of TP53, CDKN2A/B, SMAD4, PTEN and ARID1A were significantly higher in MTAP-loss PDAC. However, previously-described biomarkers of IO efficacy (MSI, TMB, CD274 amplification and PD-L1 expression) and resistance ( STK11, KEAP1 and MDM2) were infrequent and similar in both groups. The frequencies of other potentially targetable GA including BRCA1/2, ATM, KRAS G12C, ERBB2, BRAF, FGFR1, NF1 and PIK3CA were also infrequent and similar in both groups of PDAC patients. Amongst a database of 16558 cases of clinically advanced cancer with MTAP loss, 1538 (9.3%) featured co-alterations in MTAP and SMAD4. 52% of the MTAP/SMAD4 co-altered cases were PDAC. Conclusions: MTAP loss is associated with a distinctive concurrent genomic profile in PDAC and represents a potential new synthetic lethality-based opportunity for treatment with PRMT5 and MAT2A inhibitors. Furthermore, MTAP loss may represent an independent negative predictive biomarker for immune checkpoint inhibition in PDAC.[Table: see text]